1. Field of the Invention
This invention generally relates to segmenting pixels in an image of a wafer for defect detection.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Wafer inspection, using either optical or electron beam technologies, is an important technique for debugging semiconductor manufacturing processes, monitoring process variations, and improving production yield in the semiconductor industry. With the ever decreasing scale of modern integrated circuits (ICs) as well as the increasing complexity of the manufacturing process, inspection becomes more and more difficult.
In each processing step performed on a semiconductor wafer, the same circuit pattern is printed in each die on the wafer. Most wafer inspection systems take advantage of this fact and use a relatively simple die-to-die comparison to detect defects on the wafer. However, the printed circuit in each die may include many areas of patterned features that repeat in the x or y direction such as the areas of DRAM, SRAM, or FLASH. This type of area is commonly referred to as an array area (the rest of the areas are called random or logic areas). To achieve better sensitivity, advanced inspection systems employ different strategies for inspecting the array areas and the random or logic areas.
Intensity may be used as a feature of segmentation to group similar intensity pixels together. Then, the same set of defect detection parameters are applied to all of the pixels in the same group (intensity-based). However, this method has a number of disadvantages. For example, an intensity-based segmentation algorithm can be used when a geometry feature scatters uniformly. Often, however, this is not enough. For example, in an intensity- or sum-of-intensity-based segmentation, a wafer image can be segmented into a quiet array segment, a noisy page break segment, and a noisy intersection segment. However, defects of interest (DOIs) in a quiet segment can be missed if a quiet segment is misclassified as a noisy segment. Segments can be misclassified when the same cutline between segments leads to different segmentation in training and runtime. Such misclassification of the segments may also be bad for any pre-processing of the image such as that which removes the periodic pattern in the page break area. As such, segmentation based purely on intensity or sum of intensity is prone to instability related to intensity variation from job-to-job during runtime. Therefore, other property-based segmentation is needed.
Another method for segmenting output of a dark field (DF) inspection system is projection-based segmentation (PBS). PBS provides a relatively simple way to separate segments in regions based on the relative projected intensity in the x and y directions. Most of the time, the PBS approach works well. However, since it is used in the pre-processing part of DF wafer inspection algorithms, there are cases when the PBS segmentation result fluctuates along the side of the underlying physical structure pattern, which makes the projection-based segmentation unstable. The direct result is to mis-segment some quiet segments as noisy segments and vice versa. The impact is to cause the defect inspection to be less adaptive to local noise.
An additional method for segmenting output of a dark field inspection system is median intensity-based segmentation (MBS). MBS is more stable than PBS because most of the time the median intensity differences between the array region and page break region are substantial, which provides easier separation between array and page break. However, the segment boundaries from the MBS can be irregular, which might not correlate to the underlying physical structure pattern very well.
Accordingly, it would be advantageous to develop methods and systems for segmenting pixels in an image of a wafer for defect detection that do not have one or more of the disadvantages described above.